# Senior Machine Learning Engineer, GenAI Security

**Company:** [Reddit](https://hotfix.jobs/companies/reddit)
**Location:** Remote
**Role:** ML Engineering
**Salary:** $217k – $303k/yr
**Experience:** 5+ years
**Skills:** PyTorch, TensorFlow, Python, Go, MLOps, Kubernetes, Transformers, Neural Networks, MLflow, Airflow, Ray, Triton, Onnx, Etl Pipelines, Anomaly Detection
**Posted:** 2026-05-05

> Leads development of security-focused ML models for GenAI traffic at Reddit, owning the full ML lifecycle from data ETL to production deployment and monitoring. Requires 5+ years experience with PyTorch/TensorFlow, deep learning architectures, and MLOps for adversarial security risks.

## Job Description

## What You’ll Do

- Build and improve security-focused ML models for Reddit’s GenAI traffic, including guardrail models, semantic classifiers, anomaly detection models, and other neural network based security signals.
- Own model development end to end: define the security problem, assemble and label datasets, build ETL pipelines, engineer features, train models, evaluate quality, deploy to production, monitor performance, and retrain from production feedback.
- Use modern deep learning architectures, including **neural networks**, **transformers**, sequence models, embeddings, and model distillation where they are the right practical fit.
- Design rigorous evaluation suites for adversarial examples, hard negatives, long-context inputs, structured payloads, tool calls, multi-turn workflows, and real production traffic.
- Improve model precision, recall, latency, cost, calibration, and operational reliability for high-impact production surfaces.
- Build repeatable **MLOps** workflows for SPACE, including training pipelines, model lineage, artifact management, holdout evaluation, dashboards, rollback paths, and retraining loops.
- Partner closely with ML Infrastructure, LLM Gateway, DevX, Ads, Answers, Safety, Privacy, Compliance, and other Security teams to bring security models into real production workflows.
- Work pragmatically with Reddit’s evolving ML platform, using existing infrastructure where possible and building focused tooling when needed to keep model iteration moving.
- Translate security goals into measurable model outcomes and help partners understand tradeoffs between risk reduction, latency, false positives, and product impact.
- Provide technical direction to other engineers and serve as a go-to ML expert for GenAI Security and broader SPACE model needs.

## Who You Might Be

- **5+ years** of experience building, training, evaluating, and deploying production ML or deep learning models.
- Hands-on experience with modern ML frameworks such as **PyTorch**, **TensorFlow**, or similar.
- Strong practical understanding of the full ML lifecycle: problem definition, data ETL, feature engineering, training, evaluation, deployment, monitoring, debugging, and retraining.
- Experience building data pipelines and working with large-scale datasets.
- Experience designing rigorous model evaluations, including precision/recall/F1, false positive analysis, threshold tuning, calibration, holdout sets, regression tests, and production-quality validation.
- Experience shipping production-quality software, preferably in **Python** and/or **Go**.
- Strong communication skills and ability to explain model behavior, risk tradeoffs, and technical decisions to cross-functional partners.
- **BS degree** in Computer Science, Machine Learning, a related technical field, or equivalent practical experience.

**Experience in the following areas is a plus:**

- Applying ML to security, privacy, trust and safety, abuse prevention, adversarial ML, or GenAI security problems.
- Training or fine-tuning neural text models for complex inputs such as long-context prompts, structured payloads, code-like content, multi-turn interactions, or tool calls.
- Production MLOps or model serving systems such as **Airflow**, **Ray**, **MLflow**, **Triton**, **ONNX**, **Kubernetes**, or similar.
- Improving model quality through labeling strategy, hard-negative mining, synthetic data generation, distillation, or active learning.

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